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.1em plus1ex minus.2ex-.5emZSL for Action Recognition.
Early work [1]} was restricted to cooking activities, using script data to transfer to unseen classes.
Gan et al. [2]} consider each action class as a domain, and address semantic representation identification as a multi-source domain generalization problem.
Manually specified semantic representations are simple and effective [3]} but labor-intensive to annotate. To overcome this, the use of label embeddings has proven popular, as only category names are needed. Some approaches use common embedding space between class labels and video features [4]}, [5]}, pairwise relationships between classes [6]}, error-correcting codes [7]}, inter-class relationships [8]}, out-of-distribution detectors [9]}, and Graph Neural networks [10]}. In contrast, we are learning to optimize centroids of visual semantic representations that generalize better to unseen classes.
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